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Designing the Search User
Interface
Dr. Marti HearstUC Berkeley
Enterprise Search Summit May 11 2010
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Search from the Human Perspective
Search interfaces should respect cognitive functions:
Perception
Attention
Memory
Language
Reasoning / Problem Solving
Feelings
Sociability
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Cognitively-oriented Guidelines
Attention Provide an experience of “Flow”
Show search results immediately
Support rapid response
One channel per mode (auditory, visual, tactile, etc)
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Cognitively-oriented Guidelines
Planning / Reasoning Provide appropriate, timely feedback
Support “orienteering” search techniques Show informative document surrogates Highlight query terms Provide sorting of results Show query term suggestions
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Cognitively-oriented Guidelines
Memory Support Recognition over Recall
Suggest the search action in the query form Support simple history mechanisms Integrate navigation and search
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Recognition over Recall
It is easier to recognize some information than generate it yourself. Learning a foreign language
Recognize a face vs. drawing it from memory
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Query Suggestion Aids
Early systems showed huge numbers of choices Often machine-generated
Often required the user to select many terms
Newer approaches Can select just one term; launches the new query
Queries often generated from other users’ queries Have good uptake (~6% usage) Anick and Kantamneni,
2008
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Dynamic Query Suggestions
Shown dynamically, while entering initial query
http://netflix.com
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Dynamic Query Suggestions
Augmenting suggestions with images or faceted classes.
http://www.imamuseum.org/
http://nextbio.com
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Post-Query Suggestions
Shown statically, after the query has been issued.
http://bing.com
http://yahoo.com
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Suggesting Destinations
Record search sessions for 100,000’s of users For a given query, where did the user end up?
Users generally browsed far from the search results page (~5 steps)
On average, users visited 2 unique domains during the course of a query trail, and just over 4 domains during a session trail
Show the query trail endpoint information at query reformulation time
Query trail suggestions were used more often (35.2% of the time) than query term suggestions (White et al. 2007)
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Showing Related Documents
Can be a “black box” and so unhelpful But in some circumstances, works well
Related articles in a search tool over biomedical text
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Faceted Breadcrumbs
http://www.boxesandarrows.com/view/faceted-finding-with
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Faceted Breadcrumbs
http://blog.linkedin.com/2010/03/05/designing-linkedin-faceted-search/
They do a good job of integrating term suggestions and faceted navigation
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Integrating Navigation and Search
Key points:
Show users structure as a starting point, rather than requiring them to generate queries
Organize results into a recognizable structure Aids in comprehension
Suggests where to go next within the collection
Eliminate empty results sets
Techniques:
Flat lists of categories
Faceted navigation
Document clustering
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Cognitively-oriented Guidelines
Language Address the vocabulary problem
Automatically match alternative ways of expressing concepts
Language + Memory: Query term suggestions Overcome anchoring tendencies
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Multiple Means of Expression
People remember the gist but not the actual words used.
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Multiple Means of Expression
People can agree on the meaning of a label, even though absent other cues they generate different labels.
The probability that two typists would suggest the same word for a given function: .11
The probability that two college students would name an object using the same word: .12. (Furnas et al. 1987)
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The Vocabulary Problem
There are many ways to say the same thing. How much does that camera cost?
How much for that camera?
That camera. How much?
What is the price of that camera?
Please price that camera for me.
What're you asking for that camera?
How much will that camera set me back?
What are these cameras going for?
What's that camera worth to you?
The interface needs to help people find alternatives, or generate them in the matching algorithm.
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The Problem of Anchoring
Ariely discusses this in Predictably Irrational Tell people to think of the last 2 digits of their
SSN Then have them bid on something in auction The numbers they thought of influenced their bids
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The Problem of Anchoring
Anchoring in search Start with a set of words Difficult to break out and try other forms of
expression Example from Dan Russell:
Harry Potter and the Half-Blood Prince sales Harry Potter and the Half-Blood Prince amount sales Harry Potter and the Half-Blood Prince quantity sales Harry Potter and the Half-Blood Prince actual quantity sales Harry Potter and the Half-Blood Prince sales actual quantity Harry Potter and the Half-Blood Prince all sales actual quantity all sales Harry Potter and the Half-Blood Prince worldwide sales Harry Potter and the Half-Blood Prince
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Cognitively-oriented Guidelines
Perception Get small details right
Apply visual cues only where appropriate
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Properties of Text
Reading requires full attention. Reading is not pre-attentive. Can’t graph nominal data on axes.
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Pre-attentive Properties
Humans can recognize in under 100ms whether or not there is a green circle among blue ones, independent of number of distractors.
This doesn’t work for text.
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Text is NOT Preattentive
SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCSUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
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Quantitative Data is Easy to Visualize
Auto data:Comparing Model yearvs. MPG by Country
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Nominal Data is Difficult to Visualize
Auto data:Comparing MPG by Model name by Country
A non-sensical visualization.
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Search Results: Thumbnail Images of Pages
Dziadosz and Chandrasekar, 2002 Showing thumbnails alongside the text made the participants much more likely to assume the document was relevant (whether in fact it was or not).
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Search Results: Thumbnail Images of Pages Results tend to be negative.
E.g., Blank squares were just as effective for search results as thumbnails, although the subjective ratings for thumbnails were high. (Czerwinski et al., 1999)
BUT People love visuals, Technology is getting better (see SearchMe) Making important text larger improves search for some
tasks (Woodruff et al. 2001)
Earlier studies maybe used thumbnails that were too small. (Kaasten et al. 2002, browser history)
Showing figures extracted from documents can be useful. (Hearst et al. 2007)
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Search Results: Thumbnail Images of Pages
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Search Results: Thumbnail Images of Pages
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Cognitively-oriented Guidelines
Feelings Strive for aesthetically pleasing designs
Avoid “punishing” situations Empty results sets Error messages instead of paths towards solution
Flow again
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Cognitively-oriented Guidelines
Sociability Make actions of people visible
Support collaboration
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Future Trends: Social Search
Social ranking (see also Ch.9, Personalization) Explicitly recommended
Digg, StumbleUpon
Delicious, Furl
Google’s SearchWiki
Implicitly recommended Click-through
People who bought…
Yahoo’s MyWeb (now Google Social S earch)
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Collaborative Search
Pickens et al. 2008
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Far Future Trend: Dialogue
We’re still far away. SIRI is promising as a move
forward; based on state-of-the-art research.
Thank you!
Full text freely available at:http://
searchuserinterfaces.com